Data parameters [1]} introduced learnable temperature parameters for each instance and class in the dataset.
These parameters controlled the gradient contribution for each data point, and were learnt using gradient descent.
In similar spirit, [2]} introduced learnable robustness parameter per data point, which generalized different regression losses.
Both of these works learn parameters per data point for robust estimation in classification or regression. In comparison, our formulation is more
generic and can admit any differentiable loss function. More importantly, due to its meta-learning framework, our approach allows for robust estimation of noise as
well as personalization.
